metadata

language:
  - hy-AM
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-1b-hy-cv
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice hy-AM
          args: hy-AM
        metrics:
          - type: wer
            value: 10.92896174863388
            name: WER LM
          - type: cer
            value: 2.3773394031360646
            name: CER LM
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: hy
        metrics:
          - name: Test WER
            type: wer
            value: 19.942816297355254
          - name: Test CER
            type: cer
            value: 7.332618465282714

Wav2Vec2-XLS-R-1b-hy

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/HY/NOIZY_STUDENT_3/ - NA dataset. It achieves the following results on the evaluation set:

Loss: 0.1827
Wer: 0.2389
Cer: 0.0427
Wer LM: 0.1093
Cer LM: 0.0238

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 8e-05
train_batch_size: 16
eval_batch_size: 64
seed: 842
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
training_steps: 3200
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
4.0311	3.51	200	0.7943	0.8981	0.2374
1.4388	7.02	400	0.2546	0.3821	0.0658
1.0949	10.53	600	0.2201	0.3216	0.0573
1.0279	14.04	800	0.2250	0.3271	0.0583
0.9923	17.54	1000	0.2074	0.3111	0.0543
0.972	21.05	1200	0.2165	0.2955	0.0536
0.9587	24.56	1400	0.2064	0.3017	0.0535
0.9421	28.07	1600	0.2062	0.2884	0.0519
0.9189	31.58	1800	0.2014	0.2822	0.0507
0.8919	35.09	2000	0.1952	0.2689	0.0488
0.8615	38.6	2200	0.2020	0.2685	0.0480
0.834	42.11	2400	0.2001	0.2654	0.0467
0.8056	45.61	2600	0.1935	0.2498	0.0448
0.7888	49.12	2800	0.1892	0.2451	0.0446
0.761	52.63	3000	0.1884	0.2432	0.0441
0.742	56.14	3200	0.1827	0.2389	0.0427

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.2.dev0
Tokenizers 0.11.0